Method for Detecting or Monitoring Sepsis by Analysing Cytokine mRNA Expression Levels

ABSTRACT

The present invention relates to a method for identifying patients who are likely to develop sepsis in response to infection, a method for monitoring the progress of sepsis in a patient and to an assay kit for identifying patients who are likely to develop sepsis and/or monitoring the progress of sepsis.

Overwhelming infection with resultant multiple organ failure, which has been termed the ‘sepsis syndrome’ [1], is a devastating illness, and is a common Intensive Care Unit (ICU) admission diagnosis, with an incidence of 3 per 1000 population per annum [2]. The sepsis syndrome has been characterised as a dysregulation of inflammation in response to infection, with life threatening organ failure attributable to a combination of excessive cytokine mediated inflammation, disseminated coagulopathy and disruption of the integrity of micro-vascular endothelium [3].

Of particular importance to the hosts' response to an invading pathogen is the induction of different elements of the CD4⁺ T helper cell population. The distal cytokine response to infection is regulated primarily by the induced CD4⁺ T cell population, which in turn is determined by the antagonistic interaction of the cytokines Interleukin 12 (IL-12) and Interleukin 4 (IL-4). These cytokines induce naïve CD4⁺ T cells to differentiate phenotypically into T Helper 1 (Th1) or Th2 cells respectively [4, 5]. Interleukin-6 (IL-6) also plays a role in Th1/Th2 differentiation promoting Th2 differentiation primarily by inducing IL-4 production, whilst inhibiting Th1 differentiation by an IL-4 independent mechanism [6].

The Th1 cellular response is generally induced by, and particularly effective against, intracellular pathogens and those that activate macrophages and natural killer (NK) cells. A Th1 response is associated with enhanced cell-mediated immunity and is therefore the appropriate response expected in the face of most common septic insults. Lymphocytes demonstrating a Th1 response pattern characteristically produce interferon-gamma (IFNγ), a pleiotrophic cytokine which functions primarily in optimising the bactericidal activity of phagocytes.

In contrast Th2 biased responses are classically responsible for defence against helminthic and arthropod infections and are an essential component of allergic type reactions. Mature Th2 cells preferentially secrete a number of cytokines, including Interleukin 10 (IL-10). The antibodies stimulated by this response, however, do not promote phagocytosis or activate complement efficiently.

There is a need to determine more clearly the cellular response to an infection and the factors which affect the response.

STATEMENTS OF INVENTION

According to the invention there is provided a method for identifying patients who are likely to develop sepsis in response to an infection, the method comprising determining the respective mRNA for a plurality of biological markers in a sample from a patient.

The biological markers may be cytokines. In one embodiment of the invention the cytolines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.

The invention also provides a method for monitoring the progress of sepsis in a patient, the method comprising determining the respective mRNA for a plurality of biological markers in a sample from a patient. The biological markers may be cytokines. In one embodiment the cytokines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.

In another embodiment the cytokines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IL-17, and IL-6.

The invention may also provide a method comprising the steps of:—

-   -   obtaining a sample;     -   extracting messenger RNA (mRNA);     -   synthesising complementary DNA (cDNA); and     -   amplifying and quantifying cDNA for a biological marker(s).

The test sample may be a blood sample. In one embodiment of the invention the test sample are mononuclear cells from a peripheral blood sample. Alternatively, the test sample may be white cells isolated in the Buffy coat layer of a peripheral blood sample.

Preferably the blood sample may be lysed prior to extracting mRNA.

In one embodiment of the invention the biological marker(s) may be cytokines.

In one embodiment of the invention the cytokines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, IL-17, and IL-6.

In another embodiment of the invention the cytokines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IL-17, and IL-6.

In one embodiment of the invention the biological marker(s) may be amplified and quantified using real time polymerase chain reaction.

In one embodiment of the invention the mRNA may be measured in absolute terms by reference to a calibration curve constructed from a standard sample of DNA and normalised to a house-keeping gene. In one embodiment the house keeping gene may be β actin.

The invention may also provide a method for treating sepsis in a patient comprising monitoring the progress of sepsis by a method as hereinbefore described and, dependent on the level of mRNA of the biological marker, administering a medicament such as IFNγ. Alternatively the medicament may comprise a medicament which blocks or antagonises the effect of Interleukin 6. In another embodiment the medicament may be a medicament which blocks or antagonises the effects of Interleukin 6 and Interleukin 10.

This invention may also provide a method of stratifying patients with sepsis by risk of mortality, and thus provides a method of identifying patients most likely to respond to a novel therapy for sepsis, and also least likely to experience toxicity from any novel therapy for sepsis.

The method(s) of the present invention may further comprise the step of determining the ratio of mRNA levels between the biological markers. For example the biological markers may be represented as a ratio between IL-10 mRNA and interferon gamma mRNA, with this ratio ranging from about 1 to 6, such as about 1.8 to 4.52, for example about 2.85. An additional biologic marker may be the ratio between IL-23 and IL-27 with this ratio ranging from about 0.05 to 4, such as about 0.13 to 2.6, for example about 1.45.

The present invention further provides for a method for prophylaxis or treatment of patients who are likely to develop sepsis in response to infection comprising identifying such patients by a method as described above and administering a medication or providing further intervention based on the determination of mRNA levels for a plurality of biological markers.

Alternatively, the present invention provides a method for prophylaxis or treatment of patients with sepsis comprising monitoring the progress of sepsis by a method as described above and administering a medication or providing an intervention based on the determination of mRNA levels for a plurality of biological markers.

In a further embodiment the present invention may provide a method for testing a medicament or other intervention suitable for prophylaxis or treatment of sepsis comprising the steps of identifying the response to a medicament or intervention using the method as described above and selecting a medicament or intervention based on the response results. Preferably the invention also provides a medicament or other intervention identified as a result of the method of testing a medicament or other intervention suitable for prophylaxis or treatment of sepsis.

The invention further provides an assay for identifying patients who are likely to develop sepsis and/or monitoring the progress of sepsis in a patient comprising the steps of:—

-   -   obtaining a sample;     -   extracting messenger RNA (mRNA);     -   synthesising complementary DNA (cDNA); and     -   amplifying and quantifying cDNA as a surrogate for specific         cytokine mRNA for a biological marker(s).

One of the advantages associated with a kit in accordance with the present invention is the ability to predict outcome in response to infection, specifically whether patients with an identified infection will subsequently develop a life threatening illness, or will recover. The importance of this prediction lies in the attendant capability to predict duration of hospitalisation in patients with infection and thereby shorted hospitalisation. In developed medical systems hospitalisation is extremely expensive, and the savings related to reduction in hospital bed days in patients with infection would justify the cost of a single, or even repeated, laboratory testing.

In one embodiment of the invention the mRNA values obtained may be inserted in a logistical model to predict patients who develop severe sepsis or tolerate infection using a logistic regression analysis. Preferably the model generates the respective probabilities of developing severe sepsis and/or of tolerating infection and the ratio of these probabilities.

In one embodiment of the invention the mRNA values obtained may be dichotomised by logistic regression analysis or recursive partitioning, and characterised as either high or low, and then these categories are used in combination, to construct a scoring system, which is predictive of the occurrence of sepsis in response to infection, and furthermore may be predictive of the survival or non survival in patients who develop sepsis.

In one embodiment of the invention the test sample may be a blood sample. Preferably the test sample is mononuclear cells from a peripheral blood sample.

The biological marker(s) may be cytokines.

In one embodiment of the invention the cytokines may be selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.

Preferably the mRNA of these cytokines may be detected and quantified using real time polymerase chain reaction.

The invention further relates to a method for predicting the onset of critical illness (or sepsis, or severe sepsis or septic shock) in response to infection and the prediction of the progress of the sepsis syndrome. The method may be based on measurement of cytokine mRNA from peripheral blood mononuclear cells contained in the Buffy Coat layer prepared from peripheral blood.

The mRNA may be measured by quantitative real time polymerase chain reaction, normalised to a house keeping gene and also normalised to a reference or calibration curve for each individual cytokine.

The cytokines involved may include TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.

The assay of the present invention may be used in the design of pharmaceutical studies of patients with infection and or sepsis, in order to stratify patients by severity of illness, predict a likely outcome, and use this information in the study design and subsequent analysis.

The prediction of the likelihood of developing severe sepsis, or septic shock or critical illness in response to infection, may be based on a logistic regression analysis of cytokine mRNA, with a risk stratification based on the relative probabilities of developing and not developing critical illness (or sepsis, or severe sepsis or septic shock) in response to infection.

The risk of persistent septic shock versus the risk of resolution of septic shock may be based on a cluster analysis.

mRNA levels for the cytokines listed in the present invention and/or ratios between the various cytokine mRNA levels, as measured by real time PCR and normalised against a house keeping gene and further calibrated against a reference curve derived from a cytokine PCR standard, other than those already stated may be used in the prediction of outcome in response to infection and outcome in patients with sepsis.

The accuracy and/or predictive capacity of this invention may be further improved by isolation of specific subgroups of white cells from peripheral blood samples and use of these cell subgroups in real time PCR assays. These cell groups include purified isolates of peripheral blood mononuclear cells, CD4+ T cells, Natural Killer cells and Natural Killer T Cells.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more clearly understood from the following description of two embodiments thereof, given by way of example only with reference to the accompanying drawings in which:—

FIG. 1 is a graph showing the results of the logistic fit for a group of patients against the relative risk. Those patients likely to develop sepsis are indicated by the dot to the left of the curve while those who are unlikely to develop sepsis are shown by the asterisk to the right of the curve (Appendix 1 and 2 give the statistical analysis);

FIG. 2 is a diagram showing the risk of developing sepsis in response to infection increasing from 16% to 100% as the empiric score increase from 0 to 3;

FIG. 3 is a diagram showing the characterisation of Interleukin-10 mRNA levels as high or low in cases of sepsis and non sepsis (Appendix 3 gives the statistical analysis);

FIG. 4 is a graph showing the cut off limits for characterising Interleukin-10 as high or low (Appendix 4 gives the statistical analysis);

FIG. 5 is a graph showing the cut off limits for characterising Inteferon gamma mRNA levels as low and high (Appendix 5 gives the statistical analysis);

FIG. 6 is a diagram showing the characterisation of interferon gamma mRNA levels as high or low in cases of sepsis and non sepsis (Appendix 6 gives the statistical analysis);

FIG. 7 is a graph showing one-way analysis of IL-23 mRNA in cases of sepsis and non-sepsis (Appendix 7 gives the statistical analysis);

FIG. 8 is a mosaic plot of contingency analysis of low v high IL-23 by sepsis and non-sepsis (Appendix 8 gives the statistical analysis);

FIG. 9 is a graph showing one-way analysis of IL-23 mRNA by low v high IL-23 (Appendix 9 gives the statistical analysis);

FIG. 10 is a graph showing analysis of IL-27 mRNA by group (Appendix 10 gives the statistical analysis);

FIG. 11 is a graph showing analysis of IL-27 mRNA by low v high IL-27 (Appendix 11 gives the statistical analysis);

FIG. 12 is a mosaic plot of contingency analysis of low v high IL-27 by sepsis and non-sepsis (Appendix 12 gives the statistical analysis);

FIG. 13 is a mosaic plot of contingency analysis of score by Sepsis v Non Sepsis (Appendix 13 gives the statistical analysis);

FIG. 14 is a mosaic plot of contingency analysis of Score IL-00, 23 and Interferon gamma By Sepsis v Non Sepsis (Appendix 14 gives the statistical analysis);

FIG. 15 is a graph showing analysis of the ration of IL-10 to Interferon gamma based on outcome of patients (Appendix 15 gives the statistical analysis);

FIG. 16 is a graph showing analysis of the ration of IL-27 to IL-23 based on outcome of patients (Appendix 16 gives the statistical analysis);

FIG. 17 is a mosaic plot of contingency analysis outcome by risk of mortality (Appendix 17 gives the statistical analysis);

FIG. 18 is a graph showing the temporal pattern of survival in patients with sepsis (Appendix 18 gives the statistical analysis);

FIG. 19 is a diagram showing analysis of outcome by mortality risk (Appendix 19 gives the statistical analysis);

FIG. 20 is a graph showing the temporal pattern of survival in patients with sepsis (Appendix 20 gives the statistical analysis);

FIG. 21 is a diagram showing the restriction map and multiple cloning site for pDNR-LIB vector,

FIG. 22 is a diagram of the MCS, multiple cloning site of FIG. 21. The stuffer fragment is replaced by the IL27 cDNA insert. Unique restriction sites are shown in bold or in grey;

FIG. 23 a shows an RNA gel with 1 kb ladder and plasmid obtained after miniprep; and

FIG. 23 b shows an RNA gel with 1 kb ladder and digested plasmid preparation

DETAILED DESCRIPTION OF THE INVENTION

By using Quantitative real-time polymerase chain reaction (QRT-PCR) we have found a pattern of cytokine gene expression associated with both severe sepsis and with infection in the absence of critical illness. We have also been able to determine whether there was a characteristic cytokine response pattern linked to the occurrence and resolution of septic shock.

We found that the patient response to infection was associated with distinct patterns of cytokine mRNA production. Whereas Interferon gamma (IFNγ) mRNA, the signature cytokine for the Th1 response, was greatest in patients who tolerated infection with relative impunity, lesser IFNγ mRNA was associated with both the occurrence of severe sepsis in response to infection and also with poor outcome, that is excess mortality and persistence of septic shock, in these ICU patients. Thus IFNγ mRNA levels can be used to predict the occurrence of sepsis, or the non occurrence of sepsis, in response to infection. IFNγ mRNA levels can also be used to predict outcome in patients who develop sepsis, as lesser IFNγ mRNA is related to persistent shock and excess mortality rates.

In contrast, greater IL-10 mRNA levels were linked with the occurrence of severe sepsis in response to infection. As IL-10 is a Th2 cytokine, this indicates that the occurrence of severe sepsis may be linked to an imbalance in cytokine production secondary to infection, with a dominant Th1 response providing the optimal defence against infection.

The maturation of naïve CD4⁺ T cells into the appropriate Th1or Th2 phenotype is essential to launch an effective host response to a bacterial infection. Therefore, it is plausible that inter-individual variation in the gene expression of the Th1 inducing cytokines, IL-12 and IFNγ, the Th2 inducing cytokines IL-10 and IL-6, and the prototypic pro-inflammatory cytokine TNFα, may be linked to both the clinical presentation and the subsequent outcome of patients with infections.

IL-12 mRNA levels were unrelated to the occurrence of severe sepsis, or to outcome in these ICU patients. This is somewhat surprising when one considers the importance of IL-12 in T cell differentiation. IL-12 is secreted predominantly by antigen presenting cells and acts on naïve CD4⁺ cells, via the transcription factor STAT 4, to promote differentiation into a Th1, IFNγ producing, cell-type [7]. In animal models this effect is quite marked, with STAT 4 knockout mice displaying a deficit in bacterial clearance, which is partially reversed by exogenous IFNγ[8]. Curiously, the impaired immune response observed in this STAT 4 knockout model appears to be qualitative rather than quantitative, as the mice can mount an adequate leukocyte response to infection, but with inadequate bacterial clearance.

Furthermore, IL-12 therapy increases survival in animal models of sepsis [9]. A similar phenomenon has been observed in humans with sepsis, with lesser production of IL-12, specifically in response to endotoxin, predictive of death from severe sepsis after major surgery.

Surprisingly however, we found a lack of variability in IL-12 levels between groups indicating that, within the cytokine cascade, the basis for an inadequate Th1 response lies somewhere between IL-12 and IFNγ. However, from the data provided below, IL-12 mRNA may prove a useful marker in distinguishing patient response to infection in larger groups of patients, as IL-12 mRNA was greater in patients with infection who did not develop sepsis than in patients who develop sepsis in response to infection.

Although the ICU group had elevated TNFα mRNA levels in comparison to the controls, these levels were less than that observed in the bacteraemic group. The exaggerated TNFα response observed in patients with infection, in contrast to control volunteers, may represent an appropriate and protective response. This concept, that pro-inflammatory cytokines are beneficial to patients with infection, may in part account for the adverse outcome observed in prior studies of TNFα antagonists in patients with septic shock [10]. The discordant patterns of TNFα and IFNγ mRNA production observed between the ICU and the bacteraemic groups may be explained by the different cellular origins of these cytokines. TNFα is produced by a wide variety of cell types, primarily macrophages, whereas IFNγ is produced primarily by Th1 cells and NK cells. Alternatively, it is plausible that the reduction in IFNγ production may be a direct consequence of the increased levels of IL-6 observed in these patients, as IL-6 is a recognised inhibitor of Th1 differentiation [6].

QRT-PCR has recently been used to study cytokine gene expression in septic shock in a human study [11, 12]. The expression of the MHC class II genes were said to be down regulated in patients with sepsis and that non-survivors of septic shock had greater IL-10:IL-1 gene expression ratios. The study focused on the outcome of patients with septic shock, and did not examine the relation between IL-10 mRNA and the occurrence of sepsis in response to infection. Furthermore this study did not quantify or analyse IFNγ mRNA levels. We have quantified cDNA levels as a surrogate for specific mRNA by QRT-PCR thereby providing specific data for mRNA levels in a given sample.

We found an increase in IL-10 mRNA levels in septic patients when compared to controls. However we found that patients who are tolerant of infection do not produce excess IL-10 mRNA. However, in contrast to previous studies, we failed to observe a link between increase in mortality and excess IL-10 mRNA levels [11]. As IL-10 mRNA levels are greater in patients who develop sepsis in response to infection, and as IL-10 mRNA levels are not increased in patients with infection, then IL-10 mRNA levels can be used to predict response to infection in terms of the occurrence or non occurrence of sepsis in response to infection. Furthermore a combination of IFNγ and IL-10 levels can be used to accurately predict the occurrence or non concurrence of sepsis in response to infection. In addition, in patients with infection and with elevated IL-10 mRNA levels, then in these patients the therapeutic administration of an IL-10 antagonistic medication would be indicated for the prevention of subsequent occurrence of sepsis.

We found that greater IL-6 levels were associated with lesser TNFα and IFNγ mRNA levels in a patient group with poor outcome. IL-6 is a Th2 cytokine; it activates the acute phase response and controls switching of immunoglobulin subclasses. Despite these pro inflammatory actions IL-6 may not possess significant bactericidal activity, as witnessed by IL-6 knockout mice not experiencing excess mortality in animal models of peritonitis [13]. Yet IL-6 production is undoubtedly of importance in patients with sepsis, as high IL-6 levels are predictive of excess mortality, with certain IL-6 haplotypes linked with greater IL-6 production and greater severity of organ failure in patients with severe sepsis [14].

However, IL-6 also acts to modulate the balance between the Th1 and Th2 response to infection, promoting the Th2 response via both IL-4 dependant and independent mechanisms, and by inhibiting the Th1 response [6]. As IFNγ is the prototypic Th1 cytokine, with a pivotal role in generating cell mediated bactericidal activity, IL-6 may actually impair phagocytic bactericidal activity by inhibiting IFNγ production. This particular phenomenon is specifically evident in the context of mycobacterial disease where IL-6 has been demonstrated to inhibit IFNγ production and associated bactericidal activity [15]. Moreover, this phenomenon may represent the basis for the linkage of greater IL-6 protein levels with lesser TNFα and IFNγ mRNA observed in our patients with persistent shock and greater mortality.

Thus, IL-6 may generate excess inflammation while simultaneously impairing bactericidal activity. Thus a therapeutic intervention in patients with sepsis, severe sepsis and or septic shock, which aimed to antagonise the effects of interleukin 6 appears indicated in any general population of patients with sepsis, severe sepsis or septic shock, but would be specifically indicated in any subgroup of these patients with lesser IFNγ gene expression as assayed by mRNA levels and specified in this invention.

Interleukin 12 is of particular interest in the elaboration of a cytokine mediated inflammatory response to sepsis, as this cytokine regulates interferon gamma production by cells of both the innate and adaptive immune systems, and IL-12 gene deletion experiments indicate that IL-12 has an important role in the clearance of pathogenic bacteria. Furthermore IL-12 is a hetero-dimer, sharing a common p40 subunit with a family of the interleukins 18, 23 and 27, which may also be involved in the cytokine mediation of the immune response to sepsis.

IL-18 acts as a cofactor, along with IL-12, to induce Th1 development Whilst not essential for Th1 differentiation, IL-18 optimises IFNγ production, having a synergistic role in the presence of IL-12. However, the precise role of IL-18 in human sepsis remains unclear as it has also may act as or Th2 polarising cytokine in certain circumstances. The discovery that IL-18/IL-12 deficient mice retained the ability to produce Th1 cells indicated alternative pathways of Th1 development. This heralded the discovery of IL-23, a heterodimer of the p40 subunit of IL-12 and a p19 subunit, which functions similarly to IL-12 and induces IFNγ activity. However, in contrast to IL-12, which acts primarily on naïve T cells, IL-23 induces proliferation of memory T cells and therefore promotes end stage inflammation. IL-27 is a cytokine structurally related to the IL-12 family, which was originally ascribed pro-inflammatory properties with Th1 inducing activity. Lastly some members of this family of cytokines are pleiotrophic, with IL-23 and IL-27 acting respectively to augment or repress cytokine production by CD4 cells with a Th17 phenotype.

We hypothesise that alterations in interferon gamma regulators other than IL-12 may prove important in the human response to infection. We demonstrate herein that the occurrence of sepsis and outcome from sepsis was linked with distinct patterns of mRNA production for, IL-23 and IL-27.

There are obvious therapeutic implications to the hypothesis that both severe sepsis and late septic shock are related to a relative pro-inflammatory cytokine deficiency. IFNγ and IL-12 are commercially available and might be of benefit to patients who develop severe sepsis in response to an infective insult. The benefits may be more obvious in those patients who develop persistent shock from severe infection, particularly if documented insufficiency in IFNγ gene expression is available. Many case series document the beneficial use of exogenous IFNγ in leishmaniasis, leprosy and multi-drug resistant tuberculosis. The therapeutic use of IFNγ in patients with sepsis has been less extensively reported. Two studies, one recruiting consecutive patients following traumatic injury and another recruiting patients after thermal injury failed to show any survival benefit from therapy with subcutaneous IFNγ [16,17]. However, a case series of patients with severe sepsis coupled with documented monocyte suppression appeared to benefit from daily subcutaneous IFNγ [18]. Similarly, trauma patients with diminished immune responsiveness, as measured by monocyte MHC class II receptor status, were less likely to develop nosocomial pneumonia when treated with inhaled IFNγ [19].

As can be seen from this brief literature review the results of trials where Interferon gamma was used as a therapy in patients with sepsis are mixed. This may have a basis in inter individual difference in the interferon gamma gene expression as documented in this manuscript, with excess mortality observed in septic patients with deficient interferon gamma gene expression. As exogenous administration of Interferon gamma is unlikely to have any therapeutic effect in septic patients with appropriate interferon gamma gene expression, an assay, such as is documented in this manuscript which measured the degree of interferon gamma gene expression, may indicate subgroups of patients with sepsis who are most likely to benefit from exogenous administration of interferon gamma in a therapeutic regimen.

In the present invention we use the sensitivity of QRT-PCR as a method for evaluating cytokine gene expression. The QRT-PCR based mRNA assay of the invention provides unique in vivo information, which can be used as an index of the adequacy of the cytokine response to infection, and can be used to predict outcome in response to infection. Furthermore quantifying specific cytokine gene expression by measuring mRNA by real time polymerase chain reaction could be used as an indication for therapeutic antagonism of IL-6 and IL-10, and administration of exogenous IFNγ in a therapeutic intervention.

We have found that measuring mRNA by QRT-PCR in patients with infection is a very useful diagnostic tool. We found a marked association between the pattern of cytokine gene expression and the occurrence of severe sepsis, persistent shock and survival. These results raise the intriguing possibility of treating patients with persistent shock and documented inhibition of IFNγ gene expression with exogenous IFNγ. Similarly patients with sepsis and excess IL-6 and or IL-10 mRNA. could be treated with a therapeutic antagonist of these cytokines. These specific intervention could be used in isolation or as a combined therapy.

We found that cytokine mRNA levels may be used to differentiate between patients who tolerate infection with impunity, without developing the sepsis syndrome and those who are likely to develop the sepsis syndrome in response to infection. This prediction is based on the mRNA pattern for TNF, IFNγ, IL-10 IL-23 and IL27, with the mRNA measured by QRT-PCR and normalised to a house-keeping gene.

When the absolute values for mRNA copy numbers for these cytokines are used in a model to predict whether patients develop severe sepsis or tolerate infection, using a logistic regression analysis, the R² value of the model is 0.78, and receiver operator curve is 0.98. This model can be used to generate the respective probabilities of developing severe sepsis and of tolerating infection, and the ratio of these probabilities. This later ratio represents a scoring system for the risk of developing severe sepsis in response to infection.

In a logistic regression analysis of this risk score against outcome, with outcome being either severe sepsis or tolerance to infection, the resulting logistic regression curve has 100% cut off points (FIG. 1). Thus, it is possible to select a score that can identify patients who will tolerate infection without developing severe sepsis with 100% specificity and with 70% sensitivity. Conversely, patients who develop severe sepsis in response to infection can be identified with 100% specificity and with 60% sensitivity.

A Model to Distinguish Between Patients with Sepsis from Patients with Infection

The scoring system described herein may be used to risk stratify patients who have infection, to identify patients who could either be treated with antibiotics for a short duration or who could receive therapy as outpatients. Alternatively, patients who are very likely to develop severe sepsis may be identified prior to the onset of a life threatening illness. These patients could receive appropriate intervention with antibiotics or surgical drainage of infected tissue.

In patients who already have developed critical illness in response to infection the mRNA pattern for the cytokines TNFα and IFNγ may be used to identify patients who are at greater risk of developing a persistent shock. This risk stratification is based on a hierarchical cluster analysis, using Wards method with standardised data, and identifies a cluster of patients who experience persistent septic shock and greater severity of organ failure. This high risk cluster have a 10 fold increased risk of persistent septic shock, and might conceivably benefit from administration of exogenous IFNγ, to ameliorate the severity or shorten the duration of septic shock.

A further scoring system is shown in Table 1 giving the results of a statistical program used to provide standard values for TNFα, IFNγ and IL-10. When the values are applied to the population and the individuals categorised, or scored, on a 0 or 1 basis depending on whether they lie in the at risk category, then a simple scoring system may be devised, ranging from 0 to 3.

TABLE 1 Count No Row % Sepsis Sepsis 0 5 1 6 83.33 16.67 1 4 11 15 26.67 73.33 2 1 24 25 4.00 96.00 3 0 16 16 0.00 100.00 10 52 62

Tests

Source DF −LogLike RSquare (U) Model 3 11.791109 0.4305 Error 58 15.600699 C. Total 61 27.391808 N 62 Test ChiSquare Prob > ChiSq Likelihood Ratio 23.582 <.0001 Pearson 27.059 <.0001

FIG. 2 is a corresponding diagram showing the risk of developing sepsis in response to infection increasing from 16 to 100% as the empiric score increases from 0 to 3. The threshold values are, in terms of crossing threshold values as part of a real time polymerase chain reaction, standardised against a house keeping gene and referenced to a standard known quantity of mRNA for each respective cytokine, as listed below:

Point Values are in terms of mRNA copy numbers pre 10 million copies of β Actin

TNFα 0 if above 21380; 1 if below 21380 IL-10 1 if above 660; 0 if below 660 IFNγ 0 if above 188; 1 if below 188

TABLE 4 The occurrence of sepsis by category of IL-10 and IFNγ mRNA. High INF and Low INF and Low IL-10 Intermediate High IL-10 Sepsis 0 18 34 Combined 21 2 0 Infection and Control groups Total 21 20 34 INF = Interferon gamma. IL-10 = Interleukin 10 High INF = >230 copies mRNA Low IL-10 is <252 copies mRNA R square = 0.5, Chi Square 79.5, p < 0.0001.

An Algorithm for the Differentiation Between Patients with Sepsis and all Other Patients

IL-10 mRNA can be used to differentiate between patients who develop sepsis and other patients by characterising IL-10 levels as either High or Low (FIG. 3). Thus IL-10 mRNA levels can be characterised as High or Low IL-10 using the intervals shown in FIG. 3.

The high and low categories of IL-10 have the limits as shown in FIG. 4. Thus there is a cut point at 426 copies of IL-10 mRNA and the 95% confidence interval of this cut off is +/−2 SEM of the difference between this cut off value and other values of IL-10 mRNA which is from 175 to 676 copies of IL-10 mRNA. (With all values of mRNA expressed per 10 million copies of β Actin)

IFNγ mRNA levels can be used to distinguish between patients with sepsis and other patients by characterising IFNγ mRNA levels as high or low using the logistic regression analysis above. These categories of IFNγ mRNA have the limits as set out in FIG. 5. Thus there is a cut off point at 240 copies of IFNγ mRNA per 10 million copies of β Actin and the 95% confidence interval of this cut off is from 100 copies to 380 copies. These categories of IFNγ mRNA can be used to distinguish between patients with sepsis and other patients, with low IFNγ observed with greater frequency in patients with sepsis (FIG. 6).

Interleukin 23

When patients with sepsis are compared with a combined group of healthy volunteers and patients with infection but without sepsis, then IL-23 is lower in patients with sepsis. Thus IL-23 may be of use in predicting response to infection. The graph of FIG. 7 shows this data. When this data was partitioned into high and low IL-23 patients, with low being patients with less 1823.259 copies of IL-23 mRNA then all patients with IL-23 less than this value were in the sepsis group (as shown in FIG. 8) The confidence internals of this characterisation of IL-23 are shown in FIG. 9, it can be seen that there is a cut off point at 1824 copies of IL-23 mRNA per 10 million copies of P Actin and the 95% confidence interval of this cut off is from 4874 to 774 copies mRNA per million copies of P Actin. Thus IL-23 levels may be of use predicting outcome in response to infection.

Interleukin 27

Referring to FIG. 10, IL-27 mRNA was greater in patients with sepsis compared with other patients. IL-27 levels could be dichotomised by recursive partitioning into high and low categories as shown in FIG. 11 There is a cut off point at 200 copies of IL-27 mRNA per 10 million copies of β Actin and the 95% confidence interval of this cut off is from 50 to 500 copies mRNA per 10 minion copies of P Actin. This categorisation of IL-27 into high and low groups can be used to distinguish between patients with sepsis and other patients, as shown in FIG. 12.

Thus IL-23 and IL-27 may be of use in predicting response to infection. However as both these cytokines regulate the CD4 Th17 cell line, then other cytokines which regulate this CD4 cell group such as IL-17 and Transforming growth factor beta-1 may be of use in predicting response to infection.

Referring to FIGS. 4, 5, 7, 9, 10, 11, 15 and 16, all values of mRNA are quoted as absolute copy numbers of mRNA with reference to a house keeping gene, which in this instance is β Actin, but other house keeping genes may be used.

Scoring Systems to Distinguish between patients with Sepsis from all other Patients

A composite score is derived from the categoric values of IFNγ, IL-10, 23 and 27:

Low IFNγ has an attributed value of 1 v 0 for the high category,

High IL-10 has an attributed values of 1 v 0 for the low category;

Low IL-23 has an attributed value of 1 v 0 for the high category; and

High IL-27 has an attributed value of 1 v 0 for the low category.

Summating these scores generates a cumulative value for risk of Sepsis. In a scoring system based on cumulative categoric values for IFNγ and IL-10 the resulting score, which ranges from 0 to 2, can be used to distinguish between patients with sepsis and other patients as shown in FIG. 13.

In the scoring system based on Interleukin 10, 23 and IFNγ, the score ranges from 0 to 3 and can be used to distinguish patients with sepsis from other patients as shown in FIG. 14.

A Model to Predict Outcome in Patients with Sepsis

This same modelling system as described above can be used to predict outcome in patients who develop severe sepsis. This model is based on Cytokine mRNA levels in a blood sample drawn on the first day of admission to intensive care.

This scoring system is based on mRNA assays for the cytokines IL-10, 23 and 27 and IFNγ, on admission to intensive care, or in the initial phases of an episode of sepsis. In particular the combination of these molecules may be used to stratify patients. For example, in patients with sepsis death, sepsis is related to greater ratio of IL-10:IFNγ mRNA, and also to greater ratio of IL-27:IL-23, as is shown in FIGS. 15 and 16 respectively.

These ratios can be dichotomised into categories of high and low by recursive partitioning as shown in Tables 2 and 3 below.

TABLE 2 Quantiles for analysis of ratio IL-10 to Interferon Gamma by category Level Minimum 10% 25% Median 75% 90% Maximum High 2.958275 3.258666 5.154364 10.12451 24.97909 51.41479 368.8188 Low 0.037236 0.100708 0.502283 1.045996 1.795874 2.745425 2.759358

Thus there is a cut off ratio of 2.85 and the 95% confidence interval of this ratio ranged from to 4.52 to 1.8.

TABLE 3 Quantiles for analysis of ratio of IL-27 to IL-23 by Category Level Minimum 10% 25% Median 75% 90% Maximum High 1.523321 1.523321 1.76629 2.798216 4.444314 8.925306 8.925306 Low 0.001791 0.008563 0.049982 0.099343 0.337921 1.037605 1.383855

Thus there is a cut off ratio of 1.45 and the 95% confidence interval of this ratio ranged from to 2.6 to 0.13.

These categories (cut off ratio for IL-10:IFNγ or IL-27:IL-23) can be attributed (divided) into a “high” or “low” group depending on whether the cut off ratio is above or below the desired ratio (such as above or below about 1.45 for the IL-27: IL-23 ratio or above or below about 2.85 for the IL-10:IFNγ ratio). A score of 1 was attributed to a High value and a score of 0 was attributed to a Low value. When the two cut off ratios for IL-1:IFNγ and IL-27:IL-23 are combined a scoring system which ranges from 0 to 2 may be generated as follows:

Total score for Score for EL-10:IFNγ Score for IL-27:IL-23 IL-10:IFNγ and ratio ratio IL-27:IL-23 ratio 0 0 0 1 0 1 0 1 1 1 1 2

This scoring system may be used to predict risk of mortality, with a total score of 0 as low risk, 2 as high risk, and 1 an intermediate risk, as shown in FIG. 17. Furthermore this scoring system can be used to predict the temporal pattern of survival in patients with sepsis (see FIG. 18). These ratios, IL-10:IFNγ and IL-27: IL-23, have been given equal weighting in the scoring system, with a score of either 0 or 1 attributed to these (cytokine) ratios. However the relative importance of these ratios may not be equal. Thus the relative importance of these ratios may vary, and the relative importance or weighting of these ratios may not be equal, with one ratio having a weight of up to 10 times greater than the weight of other ratios.

The occurrence of sepsis by category of IL-10 and IFNγ mRNA can be seen in Table 4. Therefore the use of the IL-10:IFNγ ratio and/or the IL-23:IL-27 ratio may be useful in stratifying patients into sepsis/non-sepsis groups.

When Plasma levels of IL-6 protein are added to this model, and dichotomised as high, that is greater than 746 pg/mL and attributed a risk score of 1, or Low, that is less than 746 pg/mL and attributed a risk score of 0, then this score can be added as an additional layer to the pre existing score. This generates a score, with range 0 to 3, which are linked to an increased risk of death from sepsis. FIG. 19 illustrates this increased risk. Furthermore this more complex risk score can be used to predict the temporal pattern of survival in patients with sepsis, as shown in FIG. 20. Whilst the data for IL-6 presented herein relates to the concentration of IL-6 protein, it will be apparent to a person skilled in the art that the present invention also encompasses the use of IL-6 mRNA data.

The Value of Outcome Prediction in Patients with Sepsis and Infection.

The data presented in the preceding text outlines a methodology for predicting response to infection and or outcome in patients with severe sepsis. This methodology can be used as a tool to facilitate study design in an investigation of novel therapeutic interventions in patients with sepsis. As patients may be categorised by risk of mortality, specific patient groups, most likely to benefit from a specific therapy can be identified. On this basis patients with severe sepsis, but with low risk of mortality might be excluded from the study of a novel therapy, which may have a potential for toxicity.

In patients with sepsis, the information provided by this invention, in tandem with the potential benefit of a therapy, in terms of relative reduction in mortality rate, can be used to design a study of sufficient size so as to have sufficient power as to reach a statistically significant result. Thus instead of studying a general cohort of patients with sepsis, patients would be categorised by risk of mortality upon recruitment, allocated to a specific subgroup, with each sub-grouped sized to achieve statistical significance, and outcome for these subgroups analysed separately. On this basis, this invention can be used as a tool to facilitate logical patient recruitment and study design.

Furthermore the process of risk stratification for patients with sepsis outlined herein would be of use in identifying which patient groups would be most likely to benefit from any future novel therapies, could be used to monitor response to therapy, and could be used to identify patients who are more likely to experience injurious therapy related toxicity than to derive a therapy related benefit. This process is also applicable to the investigation of therapies for infection with novel anti microbial medications. This invention proposes an algorithm, based on cytokine mRNA, which predicts the occurrence of sepsis in patients with infection, and which may act as a covariate in the prediction of likely therapeutic failure or success.

Categorising patients according to the likelihood of response to a novel therapy maybe important for example for pharmaceutical companies as at present pharmaceutical companies bringing novel therapies for sepsis to the market lack an objective index which might differentiate between potential responders and non responders. Such an index or marker would have a major impact on the conduct of clinical trials of novel therapies in sepsis, and on the subsequent application of a novel therapy in clinical practice.

The invention will be more clearly understood from the following examples thereof.

Patients

This study was conducted in St James's Hospital, Dublin, Ireland, and was approved by the institutional ethics committee. Informed written consent was obtained from each patient or a relative. A total of 62 consecutive patients of Irish descent with severe sepsis or septic shock, as defined by the American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference [1] were enrolled over the course of 12 months. All ICU patients received similar standardised care.

Severity of illness was characterised on admission to ICU using the Simplified Acute Physiology Score (SAPS2), Multiple Organ Dysfunction Score (MODS) and the Sequential Organ Failure Assessment (SOFA) scoring systems and again on day 7 with the MODS and SOFA scores.

Individual clinical and laboratory variables were collected on day 1 and day 7 of ICU stay. The recorded variables represented the most significant derangements from normal values recorded by the nursing staff over each 24 hour period. The duration of dialysis, inotrope dependence, ventilation and ICU stay was recorded for each patient. The source of infection necessitating the ICU admission, subsequent infections over the course of the ICU stay and the respective pathogenic organisms were noted. ICU death or survival to ICU discharge was recorded. Ten consecutive patients with a gram negative bacteremia, confirmed on blood culture, and with no organ failure or impending septic crisis were recruited from hospital wards. Thirteen healthy staff members served as a control group.

Exclusion Criteria

Exclusion criteria included: 1) infection with the human immunodeficiency virus; 2) patients neutropenic as a result of chemotherapy; 3) patients receiving long term treatment with corticosteroids; 4) patients receiving immunosuppressive therapy after organ transplantation; 5) non-Caucasian ethnic background.

Blood Sampling

Blood sampling was carried out within the first 24 hours of ICU admission and again 7 days later through an indwelling central venous line or by venipuncture. In bacteraemic patients, blood sampling was carried out within 24 hours of the positive blood culture being reported. Blood samples were collected from healthy controls at one time point.

Peripheral Blood Mononuclear Cells (PBMCs) were immediately purified by density gradient centrifugation of EDTA anticoagulated blood using lymphoprep (Nycomed Pharma, Oslo, Norway). Buffy coat, the fraction of a centrifuged blood sample that contains most of the white blood cells can be distinguished after centrifugation. The Buffy coat layer is the layer between the layer of clear fluid (the plasma) and the layer of red fluid containing most of the red blood cells. This thin layer in between, the buffy coat, contains most of the white blood cells and platelets.

Serum was obtained from whole blood clotted for 30 minutes at room temperature and spun at 2500 rpm for 10 minutes.

Total RNA Extraction and Reverse Transcription

Total RNA was isolated from lysed PBMC using a commercially available kit (Qiagen RNeasy kit) following the manufacturers instructions. In order to avoid amplification of contaminating genomic DNA all samples were treated with RNase-free DNase (Qiagen) for 15 minutes. The quantity and purity of extracted RNA was measured with a spectrophotometer (Eppendorf BioPhotometer, Eppendorf AG, Hamburg, Germany). The quality of the extracted mRNA was verified on an Agilent 2100 Bioanalyzer using the RNA Nano LabChip kit (Agilent Technologies, Palto Alto, Calif., USA).

Total RNA was then reverse transcribed as follows: 11.15 μl of water containing 500 ng of total RNA was first incubated at 65° C. for 10 minutes. 18.85 μl of the reverse transcription mix containing the following components were added: (1) 3 μl 0.1M DTT; (2) 4.5 μl Dimethyl Sulfoxide: (3) 2 μl 100 μM Random Primers (Invitrogen Corp, Carlsbad, Calif., USA); (4) 1.25 μl Moloney Murine Leucemia Virus Reverse Transcriptase (Invitrogen Corp); (5) 6 μl 5× First Strand Buffer (Invitrogen Corp); (6) 1.5 μl 4 mM deoxynucleotide triphosphate mix (Promega Corp, Madison Minn., USA); (7) 0.6% RNasin (Promega Corp) 10 U/μl. The samples were then incubated at 37° C. for 1 hour.

Primers and Probes

All primer and probes used in this study were synthesized at Applied Biosystems (Foster City, Calif., USA). TNFα and IL0 primers and probes were obtained as a pre-customised mix (Assay ID for TNFα is Hs00174128_ml and for IL10 is Hs00174086_ml). β-actin, IL12p35 and IFNγ primers and probes were designed and customised as per Stordeur et al [11]. The oligonucleotides for real-time PCR are listed in Table A.

TABLE A mRNA targets Oligonucleotides (5′→3′)^(a) (bp) (nM)^(b) Product size Final concentration β-actin F976: GGATGCAGAAGGAGATCACTG 90 F300; R300 R1065: CGATCCACACGGAGTACTTG P997: 6Fam-CCCTGGCACCCAGCACAATG-Tamra-p IFN-γ F464: CTAATTATTCGGTAACTGACTTGA 75 F600; R900 R538: ACAGTTCAGCCATCACTTGGA P49I: 6Fam-TCCAACGCAAAGCAATACATGAAC-Tamra-p IL12p35 F212: CTCCTGGACCACCTCAGTTTG 76 F600; R900 R287: GGTGAAGGCATGGGAACATT P234: 6Fam-CCAGAAACCTCCCCGTGGCCA-Tamra-p TNFα Supplied as a precustomised mix from Applied F900; R900; P250 Biosystems IL10 Supplied as a precustomised mix from Applied F900; R900; P250 Biosystems ^(a)F, R and P indicated forward and reverse primers and probes, respectively; numbers indicated the sequence position. ^(b)Final concentration of forward (F) and reverse (R) primers and probe (P).

IL-12 is a heterodimeric protein composed of two subunits, p40 and p35, encoded by unrelated genes. Neither subunit has biological activity alone, although a p40 homodimer may act as IL-12 antagonist [12]. Furthermore, IL-12 production by monocytes results in a 500-fold excess of p40 relative to the active heterodimmer [13]. Approximately 20-40% of the p40 in the serum of normal and endotoxin-treated mice is in the form of the homodimer [14]. As a consequence of this we chose to measure the p35 subunit as an index of the IL-12 heterodimer activity.

Real-Time PCR

The PCR reactions were carried out in an ABI Prism 7000 (Applied Biosytems). All reactions were performed either in triplicate or in duplicate. Thermocycling was carried out in a 20 μl final volume containing: (1) water up to 20 μl; (2) 10 μl Mastermix (Applied Biosystems); (3) 1, 2 or 3 μl of 6 pmol/μl forward and reverse primers (final concentration 300, 600 or 900 nM, see Table 1); (5) 1 μl of 4 pmol/μl Taqman Probe (final concentration 200 nM) or 1 μl of pre-customised primer/probe mix with default primer and probe concentrations (Table 1); (6) 0.8 μl of the standard dilution or 2.4 μl cDNA. After an initial denaturation step at 95° C. for 10 minutes, temperature cycling was initiated. Each cycle consisted of 95° C. for 15 seconds and 60° C. for 60 seconds, the fluorescence being read at the end of this second step. In total, 40 cycles were performed.

Standard Curves and Expression of the Results

The DNA standards for TNF, IL-10, β-actin, IL12p35 and IFNγ consisted of a cloned PCR product that included the quantified amplicon prepared by PCR from a cDNA population containing the target mRNA. Information on these oligonucleotide standards is given in Table B.

TABLE B Conditions for “classical” mRNA targets Oligonucleotides (5′→3′)^(a) Product size (bp) PCR^(b) IL10 F296: TTTACCTGGAGAGGTGATG 476 A = 56, Mg = 1.5 R771: TTGGGCTTCTTTCTAAATCGT TNFα F83: ACCATGAGCACTGAAAGCAT 406 A = 58, Mg = 1.5 R488: AGATGAGGTACAGGCCCTCT IFNγ F154: TTGGGTTCTCTTGGCTGTTA 479 A = 58, Mg = 1.5 R632: AAATATTGCAGGCAGGACAA β-actin F745: CCCTGGAGAAGAGCTACGA 509 A = 58, Mg = 1.5 RI253: TAAAGCCATGCCAATCTCAT IL12 F185: AGCCTCCTCCTTGTGGCTA 228 A = 59, Mg = 1.5 R412: TGTGCTGGTTTTATCTTTTGTG ^(a)F and R indicate forward and reverse primers, respectively; numbers indicate the sequence position. ^(b)Conditions, for all targets, were as follows: denaturation at 95° C. for 20 s, annealing (temperature as stated (A)) for 20 s and elongation at 72° C. for 45 s, for a total of 35 cycles. MgC12 concentration (Mg,mM) was as stated.

In order to quantify transcript levels a standard curve was constructed, for each PCR run, for each selected mRNA target from serial dilutions of the relevant standard. All standard curves showed correlation coefficients >0.99, indicating a precise log linear relationship.

The mean efficiency of the standard curves for all target cDNA was 98.75%+/−4.4%. The mRNA copy numbers were then calculated for each patient sample using the standard curve to convert the obtained crossing threshold (Ct) value into mRNA copy numbers. Results were then expressed in absolute copy numbers after normalisation against β-actin mRNA (mRNA copy numbers of cytokine mRNA per 10 million β-actin mRNA copy numbers).

Cytokine Enzyme Linked Immunosorbant Assay (ELISA)

Serum TNFα, IL10, IL6 and IFNγ concentrations were measured by ELISA (R+D systems, Minneapolis, Minn., USA) following the manufacturers instructions. The lower limit of detection for TNFα was 15.625 pg/ml, for IL10 was 46.875 pg/ml, for IL6 was 9.375 pg/ml and for IFNγ was 15.625 pg/ml. All samples were tested in duplicate.

Statistical Analysis

The Wilcoxon rank sum test was used to analyse the differences between groups for continuous variables. Change in a continuous variable over time was analysed by ANOVA for repeated measures. Categorical variables were analysed by Chi Square test and Fishers exact test as appropriate. Spearman's rank correlation coefficient was used to analyse the relationship between continuous variables. Hierarchical cluster analysis was performed with standardised data using Ward's method the results of which were analysed using the JMP statistical software package (SAS Institute, Cary N.C., US).

Real Time Polymerase Chain Reaction Methods

Cytokine mRNA was measured as an absolute value of copies of mRNA for respective cytokines, and from a single blood sample. This measurement was accomplished by using DNA standards, containing known quantities of DNA, and constructing a reference calibration RT PCR curve by the use of sequential dilutions of these standards.

Similarly reference curves for a house keeping gene, in our case □ actin was constructed so that the cytokine mRNA measure could be normalised against this house keeping gene.

The reference PCR curve which was used as a calibration tool and permitted measurement of cytokine mRNA in absolute terms as copy numbers per million copy numbers of a house keeping gene.

Preparation of DNA Standards Pre-Prepared Cytokine Standards

The DNA standards for TNFα, IL10, B-actin, IL12p35, IFNγ, IL18, IL4 and IL23p19 consisted of a cloned PCR product that included the quantified amplicon that was prepared by PCR from a cDNA population containing the target mRNA. Sequences and reaction conditions are given in Table C.

Stock solutions of standards, containing 10⁹ (IFNγ, B-actin, INFα, IL23p35 and IL4) or 10¹⁰ (IL2p35 and IL10) copy numbers per μl, were aliquoted and stored at −20° C. A dilution series from 10⁹ to 10² numbers per μl was prepared in each case and stored at 4° C. The standards were diluted in TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) containing double-stranded herring DNA (Sigma) at 10 μg/ml.

TABLE C Oligonucleotides for standard preparation Conditions for “classical” mRNA targets Oligonucleotides (5′→3′)^(a) Product size (bp) PCR^(b) IL10 F296: TTTACCTGGAGAGGTGATG 476 A = 56, Mg = 1.5 R771: TTGGGCTTCTTTCTAAATCGT TNFα F83: ACCATGAGCACTGAAAGCAT 406 A = 58, Mg = 1.5 R488: AGATGAGGTACAGGCCCTCT IFNγ F154: TTGGGTTCTCTTGGCTGTTA 479 A = 58, Mg = 1.5 R632: AAATATTGCAGGCAGGACAA β-actin F745: CCCTGGAGAAGAGCTACGA 509 A = 58, Mg = 1.5 R1253: TAAAGCCATGCCAATCTCAT 1L12 F185: AGCCTCCTCCTTGTGGCTA 228 A = 58, Mg = 1.5 R412: TGTGCTGGTTTTATCTTTTGTG IL18 F133: AGTCTACACAGCTTCGGGAAGA 113 A = 60, Mg = 1.5 R653: GTCCTGGGACACTTCTCTGAAA 1L4 F27: TAATTGCCTCACATTGTCACT 503 A = 58, Mg = 1.5 R52P: ATTCAGCTCGAACACTTTGAA ^(a)F and R indicate forward and reverse primers, respectively; numbers indicate the sequence position. ^(b)Conditions, for all targets, were as follows: denaturation at 95° C. for 20 s, annealing (temperature as stated (A)) for 20 s and elongation at 72° C. for 45 s, for a total of 35 cycles. MgC12 concentration (Mg, mM) was as stated.(For the complete procedure see Stordeur et al., 1995, PCR for IFNγ[125].

Preparation of the IL27p28 Standard

An IL 27 standard was fabricated de novo, which illustrates the methodology of constructing these standards.

IL27p28 plasmid purchased from Open Biosystems. It consisted of a 1.9 kb cDNA clone inserted into a 4.2 kb vector transformed into chloramphenicol resistant E. coli. The vector was pDNR-LIB as shown in FIG. 21. FIG. 22 shows the MCS, multiple cloning site. The stuffer fragment is replaced by the IL27 cDNA insert. Unique restriction sites are shown in bold or in grey.

Plasmid DNA was cultured by being double-streaked on a chloramphenicol LB agar plate. A sample from this was then placed in a liquid culture overnight Small and large scale plasmid DNA isolation was performed using Qiagen Mini and Midi kits, respectively. Restriction digestion procedures were performed by the methods of Sambrook et al. Following electorphoretic separation, DNA fragments were purified from gels using the QIAquick Gel Extraction Kit (Qiagen).

RNA Gels

All RNA gels were constructed by dissolving agarose in Tris-Borate-EDTA (TBE) buffer (Sigma). This was achieved by heating the mixture in a microwave to create a 1% agarose solution. Ethidium bromide was added to the gel at a final concentration of 0.5 μg/ml to facilitate visualisation of the DNA. The gel is then poured into a casting container containing a sample comb and allowed to cool to room temperature. After the gel has solidified, the comb is removed. The gel, still in its plastic tray, is inserted horizontally into the electrophoresis chamber and just covered with buffer. A voltage of 135V was then applied across to the gel for 35 minutes. An ultraviolet light box was then used to visualize ethidium bromide-stained DNA in gels.

Gel Purification

The IL27 fragment was extracted from the agarose gel using a QIAquick Gel Extraction Kit (Qiagen) following the manufacturers instructions. The band of interest is carefully removed from the gel using a scalpel. This is then placed in a spin-column which contains a DNA binding silica-gel membrane. DNA adsorbs to this membrane in the presence of high salt buffer while contaminants pass through the column as impurities are washed away. Tris elution buffer is added to the center of the QIAquick membrane and the column is left to stand for 1 minute before the DNA is eluted with the elution buffer. This increases DNA yield by up to 1.7 times. Nucleic Acid quantification then took place using the nanodrop.

Determining the Volume of Plasmid DNA Corresponding to Copy Numbers of Target Nucleic Acid Sequences

First we calculate the mass of a single plasmid molecule. Insert the plasmid size value into the formula below:

m=(n)(1.096×10⁻²¹ g/bp);

where m=mass and n=plasmid size (bp), where the size of the entire plasmid (plasmid+insert) is used in this calculation. In the case of IL27:

m = 5277  bp  (1.096 × 10⁻²¹)  g/bp   = 5.78 × 10⁻¹⁸g = mass  of  1  plasmid  molecule

We then calculate the mass of plasmid containing the CN of interest in this example 10⁻¹¹ copies:

10⁻¹¹ copies×5.78×10⁻¹⁸ g/copy

5.78×10⁻⁷ g

We then calculate the concentrations of plasmid DNA needed to achieve the CNs of interest. We divide the mass needed by the volume to be pipetted into each reaction. For this example 5 μL of plasmid DNA solution is pipetted into each PCR reaction. For 10⁻¹¹ copies;

5.78×10⁻⁷ g/5 μL=1.16×10⁻⁷ g/μL.

We then prepare a serial dilution of the plasmid DNA. We use the following formula to calculate the volume needed to prepare the 10⁻¹¹ copy standard dilution. For this example the stock solution of plasmid DNA was taken as having a concentration 1.5 μg/μL as determined by spectrophotometric analysis.

C₁V₁=C₂V₂

(1.5 μg/μL)(V ₁)=(1.16×10⁻⁷ g/μL)(100 μL)

V₁=7.73 μL

For 10¹¹ CN IL27/μL add 7.73 μL to 92.27 μL of dilutent and from this point prepare a serial dilution of the plasmid DNA.

FIG. 23 a shows an RNA gel with 1 kb ladder and plasmid obtained after miniprep and FIG. 23 b shows an RNA gel with 1 kb ladder and digested plasmid preparation.

Preparation of the IKML Standard

The Human IKBL cDNA was purchased from Invitrogen (H-X77909-M Invitrogen clone). The complete coding sequence was amplified using the following primers that inserted the restriction sites Ndel and Xho 1:

Forward primer: ACTCAGATCATATGAGTAACCCCTC Reverse Primer: TGGATCCTCGAGTCACGGTACCTTGAG

The reaction conditions for the PCR were 95° C. for 15 min; 95° C. for 30 s; 58° C. for 1 min; 72° C. for 1 min for 35 cycles.

The amplified products were gel purified using a Qiagen gel purification kit, confimmed by sequencing (Dept of Biochemistry, Cambridge University, UK) and quantified using Pico green (Cambridge Biosciences, UK).

Results

Sixty-two ICU patients, 10 bacteraemic ward patients and 13 healthy controls were consented and recruited into the study. Blood samples were available for PCR analysis from 52 of these ICU patients on the first day of critical illness and from 49 ICU patients on the seventh day of critical illness. A total of 39 of the ICU patients bad blood samples available for PCR analysis at both time points. Serum was collected for ELISA on 37 ICU patients on day 1 and 36 ICU patients on day 7. The illness severity scores and sites of infection for the ICU group are listed in Table 5.

TABLE 5 Non Survivors survivors p N 45 17 Male 24 (53%) 10 (59%) ns Age (years) 62 (19-81) 67 (39-86) ns SOFA day 1 7 (4-10) 9 (9-13) 0.003 SAPS II 40 (33-51) 49 (41-60) 0.02 Ventilated Day 1 36 (80%) 17 (100%) ns Inotropes Day 1 26 (58%) 16 (94%) 0.006 Intensive Care Stay 12 (3.5-27) 5 (3-10.5) 0.03 Site of infection Respiratory 22 (49%) 10 (59%) ns Abdominal 15 (33%) 5 (29%) ns Others 8 (18%) 2 (12%) ns

Results are presented both for the complete cohort of ICU patient Age and severity of illness scores are presented as means with the range in parenthesis. Table 6 shows the Cytokine mRNA quantification in Intensive Care Patients with Sepsis on Day 1 compared with Controls and Patients with Bacteraemia. On the first day of critical illness the ICU group showed greater TNFα and IL-10 mRNA levels and lesser IFNγ mRNA levels than the control group. The ICU group had lesser TNFα and INFγ and greater IL-10 mRNA levels in comparison with the bacteraemia group (Table 6). There was no difference in IL-12 mRNA levels between groups.

TABLE 6 Cytokine mRNA quantification in Intensive Care Patients with Sepsis on Day 1 compared with Controls and Patients with Bacteraemi ICU Day 1 Bacteraemia Control N 52 10 13 TNFα 19866 (11545-28270) 36294 (28858-72275) 577 (422-1679) p 0.0007 <0.0001 IL-10 851 (573-1638) 163 (88-363) 66 (33-81) p <0.0001 <0.0001 IL-12 2170 (923-4039) 3843 (1937-5910) 2123 (1676-5597) p 0.03 ns^(a) IFNγ 131 (76-350) 745 (285-941) 883 (617-1216) p 0.0003 <0.0001 IL-4 N 39 9 12 9 (2-28) 201 (22-512) 161 (58-262) p 0.005 0.0004 Results are expressed as mRNA copy numbers per 10 million β actin mRNA copies. All values are median and inter quartile range. All comparisons are by Wilcoxon rank sum test with p values stated as uncorrected values. All p values are for comparison with the ICU group. ^(a)non significant

Table 7 shows the relation between cytokine mRNA levels and requirement for inotrope infusions on day 7 in Intensive Care Patients with sepsis. In the ICU group, there was no association between the cytokine mRNA levels and the presence or absence of shock on day 1 of critical illness. However on the seventh day, shocked patients displayed significantly lower TNFα and IFNγ mRNA levels than the patients without shock.

TABLE 7 The relationship between cytokine mRNA levels and the presence of shock, on day 7 in the ICU group. Cytokine Shock Group Group without Shock p N 15 34 TNF 9667 (6207-17291) 19179 (10902-28543 0.001 IL-10 355 (154-452) 189 (56-312) 0.11 IL-12 2619 (2109-8014) 4619 (2669-6699) ns^(a) IFNγ 150 (70-195) 368 (196-772) 0.0004 IL-4 N 15 31 229 (26-671) 66 (15-270) ns ^(a)non significant

Results are expressed as mRNA copy numbers per million β actin mRNA copies. All comparisons are by Wilcoxon rank sum tesy. All values are stated as median with inter quartile ranges.

Results are expressed as log mRNA copy numbers per 10⁷ β actin mRNA copies. All comparisons are by Wilcoxon rank sum test. All values are stated as median with inter quartile ranges.

Seventeen of the 62 ICU patients (27%) died during the course of their ICU stay. Of the 52 patients that were analysed for mRNA production on the first day of critical illness, 13 died prior to ICU discharge. There was a non-significant trend towards greater IL-10 mRNA levels (median 3.10, inter quartile range 2.86-3.5 versus, median 2.89, inter quartile range 2.73-3.13; p=0.1) and lesser IFNγ mRNA levels (median 2.05, inter quartile range 1.82-2.15 versus median 2.26, interquartile range 1.88-2.61; p=0.07, all values expressed as mRNA copy numbers per 10 million β-actin copy numbers) in non-survivors when compared with survivors. However, when ICU patients were dichotomised between those in the highest quartile of IFNγ mRNA production on day 1 and the remainder, no deaths occurred in the highest quartile group, whereas 13 of the other 39 patients died (p=0.02).

Table 8 shows the relationship between cytokine mRNA levels in Intensive Care Patients on day 7 of critical illness and subsequent outcome. Eight of the 49 ICU patients who had blood samples analysed for mRNA. production after 7 days of critical illness died prior to ICU discharge. These 8 non-survivors had lesser TNFα mRNA levels and a trend towards lesser IL-12 and IL-10 mRNA levels in comparison to survivors.

TABLE 8 Relationship between cytokine mRNA levels in the ICU group on day 7 of critical illness and subsequent outcome. Survivors Non Survivors p N 41 8 TNFα 17329 (10766-25600) 6898 (3814-9605) 0.002 IL-10 255 (133-402) 40 (24-348) 0.08 IL-12 4672 (2619-6885) 2420 (1588-6338) ns^(a) INFγ 293 (158-677) 148 (59-565) ns IL-4 N 38 8 76 (27-281) 178 (10-954) ns

Results are expressed as mRNA copy numbers per 10 million β actin mRNA copies. All values are median and inter quartile range. All comparisons by Wilcoxon rank sum test with p values stated as uncorrected values.

Results are expressed as log mRNA copy numbers per 10⁷ □ actin mRNA copies. All values are median and inter quartile range. All comparisons with control by Wilcoxon rank sum test with p values stated as uncorrected values.

A cluster analysis, which included ICU data for day 7 TNFα and IFNγ mRNA levels, was performed and a cluster of ICU patients with reduced TNFα and IFNγ mRNA levels was characterised. Patients in this cluster were more likely to require inotropes on day 7 of critical illness, had greater SOFA scores and had a significantly higher mortality (Table 9).

TABLE 9 Cluster analysis of TNFα and IFNγ mRNA levels in the ICU group on Day 7 of critical illness Cluster 1 2 p N 31 18 TNFα 21379 (16218-28840) 6760 (2884-10715) 0.0001 INFγ 407 (190-794) 158 (93-229) 0.0004 Shock 4 (13%) 11 (61%) 0.0009 SOFA 3 (0-6) 10 (8-13) 0.0001 Outcome 2 (6.5%) 6 (33%) 0.04 (Death)

IL-6 (By Elisa in pg/ml) N 21 15 IL-6 10.7 (0-103) 142 (29-456) 0.007

Results are expressed as log mRNA copy numbers per 10⁷ β actin mRNA copies (TNFα and IFNγ) or as pg/ml (IL-6). All continuous values are quoted as median with interquartile ranges. All comparisons are Wilcoxon Rank Sum test.

The majority of ICU patients bad no detectable TNFα, IL-10 or IFNγ protein However IL-6 was present in measurable quantities on day 1 (median 306, inter quartile range 131 to 731) and day 7 (median 63, inter quartile range 3.6 to 174, all values expressed as pg/ml). On the first day of critical illness there was a negative correlation between IL-6 levels and IFNγ mRNA levels (Spearnan's Rho=−0.52, p=0.0009). On day 7 there was a negative correlation between IL-6 levels and TNFα mRNA levels (Spearman's Rho=−0.43, p=0.006). Also, on day 7, IL-6 protein levels were greater in ICU patients in the cluster with lesser TNFα and IFNγ mRNA production.

The data indicates that IL-6 acts differently to other pro-inflammatory cytokines and may antagonise a beneficial response resulting in adverse outcome. Based on the results it is envisaged that an anti-IL-6 antibody may potentially have use in conjunction with IFNγ in the preparation of a medicament for the treatment of patients with severe sepsis.

The invention is not limited to the embodiments hereinbefore described which may be varied in detail.

APPENDIX 1 Nominal Logistic Fit for Group Whole Model Test Model −LogLikelihood DF ChiSquare Prob > ChiSq Difference 21.462100 1 42.9242 <.0001 Pull 5.929708 Reduced 27.391808 RSquare (U) 0.7835 — Observations (or Sum Wgts) 62 Converged by Objective Lack Of Fit Source DF −LogLikelihood ChiSquare Lack Of Fit 60 5.9297076 11.85942 Saturated 61 0.0000000 Prob > ChiSq Fitted 1 5.9297076 1.0000 Parameter Estimates Odds Odds Term Estimate Std Error ChiSquare Prob > ChiSq Lower 95% Upper 95% Ratio Lower Odds Upper Intercept 1.50752e−7 0.809307 0.00 1.0000 −1.5561144 1.88139339 — — — Relative risk 1.00000023 0.4474755 4.99 0.0254 0.39721176 2.26336515 4.34883e15 1628335.94 2.48507e35 For log odds of No Sepsis/Sepsis Effect Wald Tests Source Nparm DF Wald ChiSquare Prob > ChiSq Relative risk 1 1 4.99415089 0.0254 Effect Likelihood Ratio Tests Source Nparm DF L-R ChiSquare Prob > ChiSq Relative risk 1 1 42.9242 0.0000

APPENDIX 2 Whole Model Test Model -LogLikelihood DF ChiSquare Prob > ChiSq Difference 21.462100 1 42.9242 <.0001 Full 5.929708 Reduced 27.391808 RSquare (U) 0.7835 Observations 62 (or Sum Wgts) Converged by Objective Parameter Estimates Term Estimate Std Error ChiSquare Prob > ChiSq Intercept 1.50752e−7 0.809307 0.00 1.0000 Relative risk 1.00000023 0.4474755 4.99 0.0254 For log odds of No Sepsis/Sepsis

APPENDIX 3 Contingency Table Sepsis v Non Sepsis By high IL-10 Count Total % Col % Row % High Low Non Sepsis 2 21 23 2.67 28.00 30.67 4.17 77.78 8.70 91.30 Sepsis 46 6 52 61.33 8.00 69.33 95.83 22.22 88.46 11.54 48 27 75 64.00 36.00 Tests Source DF −LogLike RSquare (U) Model 1 23.614651 0.4819 Error 73 25.391714 C. Total 74 49.006365 N 75 Test ChiSquare Prob > ChiSq Likelihood Ratio 47.229 <.0001 Pearson 44.037 <.0001 Fisher's Exact Test Prob Alternative Hypothesis Left <.0001 Prob(high il-10 = Low) is greater for Sepsis v Non Sepsis = Non Sepsis than Sepsis Right 1.0000 Prob(high il-10 = Low) is greater for Sepsis v Non Sepsis = Sepsis than Non Sepsis 2-Tail <.0001 Prob(high il-10 = Low) is different across Sepsis v Non Sepsis

APPENDIX 4 IL-10 Quantiles Level Minimum 10% 25% Median 75% 90% Maximum High 427.7401 517.0439 657.8845 949.4901 1710.892 3264.384 5004.377 Low 2.845719 13.31157 45.12425 89.35729 197.1628 347.0581 420.9414

There is a cut point at 426 copies of IL-10 mRNA and the 95% confidence interval of this cut off is +1-2 SEM of the difference between this cut off value and other values of IL-10 mRNA which is from 175 to 676 copies of 10 mRNA. (With all values of mRNA expressed per 10 million copies of β Actin)

APPENDIX 5 Interferon gamma Quantiles Level Minimum 10% 25% Median 75% 90% Maximum High 240.9588 304.9862 409.6467 691.7958 990.8418 1366.33 3917.509 Low 10.07806 25.09425 58.54774 104.3432 145.3965 200.1639 239.663

There is a cut off point at 240 copies of IFNγ mRNA per 10 million copies of β Actin and the 95% confidence interval of this cut off is from 100 copies to 380 copies.

APPENDIX 6 Contingency Table Interferon gamma category By Sepsis v Non Sepsis Count Total % Col % Non Row % Sepsis Sepsis High 22 15 37 29.33 20.00 49.33 95.65 28.85 59.46 40.54 Low 1 37 38 1.33 49.33 50.67 4.35 71.15 2.63 97.37 23 52 75 30.67 69.33 Tests Source DF −LogLike RSquare (U) Model 1 16.625981 0.3596 Error 73 29.604587 C. Total 74 46.230568 N 75 Test ChiSquare Prob > ChiSq Likelihood Ratio 33.252 <.0001 Pearson 28.473 <.0001 Fisher's Exact Test Prob Alternative Hypothesis Left 1.0000 Prob(Sepsis v Non Sepsis = Sepsis) is greater for low inf = High than Low Right <.0001 Prob(Sepsis v Non Sepsis = Sepsis) is greater for low inf = Low than High 2-Tail <.0001 Prob(Sepsis v Non Sepsis = Sepsis) is different across low inf

APPENDIX 7 Quantiles for IL-23 Level Minimum 10% 25% Median 75% 90% Maximum Non Sepsis 1823.259 2747.308 4894.172 8414.014 18437.34 24025.62 32538.94 Sepsis 220.4026 655.6246 1561.336 4767.355 9487.687 22787.51 47140.12 Wilcoxon/Kruskal-Wallis Tests (Rank Sums) Level Count Score Sum Score Mean (Mean − Mean0)/Std0 Non Sepsis 23 1090 47.3913 2.651 Sepsis 51 1685 33.0392 −2.651 2-Sample Test, Normal Approximation S Z Prob > |Z| 1090 2.65117 0.0080 1-way Test, ChiSquare Approximation ChiSquare DF Prob > ChiSq 7.0597 1 0.0079

APPENDIX 8 Contingency Table Sepsis v Non Sepsis By Low v High IL-23 Count Total % Col % Row % High Low Non Sepsis 23 0 23 31.08 0.00 31.08 38.98 0.00 100.00 0.00 Sepsis 36 15 51 48.65 20.27 68.92 61.02 100.00 70.59 29.41 59 15 74 79.73 20.27 Tests Source DF −LogLike RSquare (U) Model 1 6.409682 0.1718 Error 72 30.895672 C. Total 73 37.305355 N 74 Test ChiSquare Prob > ChiSq Likelihood Ratio 12.819 0.0003 Pearson 8.485 0.0036 Fisher's Exact Test Prob Alternative Hypothesis Left 1.0000 Prob(Low v High IL-23 = Low) is greater for Sepsis v Non Sepsis = Non Sepsis than Sepsis Right 0.0017 Prob(Low v High IL-23 = Low) is greater for Sepsis v Non Sepsis = Sepsis than Non Sepsis 2-Tail 0.0034 Prob(Low v High IL-23 = Low) is different across Sepsis v Non Sepsis

APPENDIX 9 IL-23 Quantiles Level Minimum 10% 25% Median 75% 90% Maximum High 1823.259 2370.565 4767.355 7096.964 14148.55 24424.69 47140.12 Low 220.4026 227.6223 420.7151 1018.412 1479.729 1654.136 1689.894

There is a cut off point at 1824 copies of IL-23 mRNA per 10 million copies of β Actin and the 95% confidence interval of this cut off is from 4874 to 774 copies mRNA per million copies of β Actin.

APPENDIX 10 IL-27 Quantiles Level Minimum 10% 25% Median 75% 90% Maximum Non Sepsis 19.26175 36.18736 80.8099 218.7316 598.7032 1248.712 2171.849 Sepsis 52.65335 173.9895 322.0002 623.8759 1312.294 2928.885 6782.582 Wilcoxon/Kruskal-Wallis Tests (Rank Sums) Level Count Score Sum Score Mean (Mean − Mean0)/Std0 Non Sepsis 20 460 23.0000 −2.923 Sepsis 46 1751 38.0652 2.923 2-Sample Test, Normal Approximation S Z Prob > |Z| 460 −2.92310 0.0035 1-way Test, ChiSquare Approximation ChiSquare DF Prob > ChiSq 8.5853 1 0.0034

APPENDIX 11 IL-27 Quantiles Level Minimum 10% 25% Median 75% 90% Maximum High 202.8909 250.1165 390.5847 652.2764 1279.947 2890.477 6782.582 Low 19.26175 29.28958 51.60408 125.5221 151.6795 190.9291 192.0399

There is a cut off point at 200 copies of IL-27 mRNA per 10 million copies of β Actin and the 95% confidence interval of this cut off is from 50 to 500 copies mRNA per 10 million copies of β Actin

APPENDIX 12 Contingency Table High v Low IL-27 By Group Count Total % Col % Non Row % Sepsis Sepsis High 10 41 51 15.15 62.12 77.27 50.00 89.13 19.61 80.39 Low 10 5 15 15.15 7.58 22.73 50.00 10.87 66.67 33.33 20 46 66 30.30 69.70 Tests Source DF −LogLike RSquare (U) Model 1 5.696549 0.1407 Error 64 34.788514 C. Total 65 40.485063 N 66 Test ChiSquare Prob > ChiSq Likelihood Ratio 11.393 0.0007 Pearson 12.153 0.0005 Fisher's Exact Test Prob Alternative Hypothesis Left 0.0011 Prob(Group = Sepsis) is greater for High v Low Il27 = High than Low Right 0.9999 Prob(Group = Sepsis) is greater for High v Low Il27 = Low than High 2-Tail 0.0011 Prob(Group = Sepsis) is different across High v Low Il27

APPENDIX 13 Contingency Table Scoring system involving IL-10 and Interferon Gamma Sepsis v Non Sepsis By score Count Total % Col % Row % 0 1 2 Non Sepsis 20 3 0 23 26.67 4.00 0.00 30.67 95.24 13.64 0.00 86.96 13.04 0.00 Sepsis 1 19 32 52 1.33 25.33 42.67 69.33 4.76 86.36 100.00 1.92 36.54 61.54 21 22 32 75 28.00 29.33 42.67 Tests Source DF −LogLike RSquare (U) Model 2 33.447486 0.4131 Error 71 47.522669 C. Total 73 80.970154 N 75 Test ChiSquare Prob > ChiSq Likelihood Ratio 66.895 <.0001 Pearson 58.335 <.0001

APPENDIX 14 Contingency Table Sepsis v Non Sepsic By Score IL-10, 23 and Interferon Gamma Count Total % Col % Row % 0 1 2 3 Non Sepsis 20 3 0 0 23 26.67 4.00 0.00 0.00 30.67 100.00 16.67 0.00 0.00 86.96 13.04 0.00 0.00 Sepsis 0 15 28 9 52 0.00 20.00 37.33 12.00 69.33 0.00 83.33 100.00 100.00 0.00 28.85 53.85 17.31 20 18 28 9 75 26.67 24.00 37.33 12.00 Tests Source DF −LogLike RSquare (U) Model 3 38.120466 0.3859 Error 69 60.673057 C. Total 72 98.793524 N 75 Test ChiSquare Prob > ChiSq Likelihood Ratio 76.241 <.0001 Pearson 63.242 <.0001

APPENDIX 15 Ratio of IL-10 to Interferon gama:Quantiles Level Minimum 10% 25% Median 75% 90% Maximum death 2.958275 3.049733 6.157191 10.39676 29.49803 240.9525 368.8188 discharge 0.037236 0.508411 1.627466 4.299748 13.83646 35.94041 74.05067 Wilcoxon/Kruskal-Wallis Tests (Rank Sums) Level Count Score Sum Score Mean (Mean − Mean0)/Std0 death 13 452 34.7692 2.261 discharge 39 926 23.7436 −2.261 2-Sample Test, Normal Approximation S Z Prob > |Z| 452 2.26117 0.0237 1-way Test, ChiSquare Approximation ChiSquare DF Prob > ChiSq 5.1608 1 0.0231

APPENDIX 16 Ratio of IL-27 to IL-23:Quantiles Level Minimum 10% 25% Median 75% 90% Maximum death 0.049982 0.050587 0.153355 0.350837 2.020705 7.206859 8.925306 discharge 0.001791 0.007722 0.024235 0.097816 0.435813 1.325813 4.444314 Wilcoxon/Kruskal-Wallis Tests (Rank Sums) Level Count Score Sum Score Mean (Mean − Mean0)/Std0 death 12 370 30.8333 2.189 discharge 34 711 20.9118 −2.189 2-Sample Test, Normal Approximation S Z Porb > |Z| 370 2.18887 0.0286 1-way Test, ChiSquare Approximation ChiSquare DF Prob > ChiSq 4.8461 1 0.0277

APPENDIX 17 Contingency Table Risk of Mortality By OUTCOME Count Total % Col % Row % death discharge High 5 1 6 8.06 1.61 9.68 29.41 2.22 83.33 16.67 Intermediate 12 33 45 19.35 53.23 72.58 70.59 73.33 26.67 73.33 Low 0 11 11 0.00 17.74 17.74 0.00 24.44 0.00 100.00 17 45 62 27.42 72.58 Tests Source DF −LogLike RSquare (U) Model 2 7.618343 0.2092 Error 59 28.799550 C. Total 61 36.417893 N 62 Test ChiSquare Prob > ChiSq Likelihood Ratio 15.237 0.0005 Pearson 13.594 0.0011

APPENDIX 18 Tests Between Groups Test ChiSquare DF Prob > ChiSq Log-Rank 23.2784 2 <.0001 Wilcoxon 23.3860 2 <.0001

APPENDIX 19 Contingency Table Mortality Risk By OUTCOME Count Total % Col % Row % death discharge 0 0 7 7 0.00 22.58 22.58 0.00 31.82 0.00 100.00 1 1 13 14 3.23 41.94 45.16 11.11 59.09 7.14 92.86 2 5 2 7 16.13 6.45 22.58 55.56 9.09 71.43 28.57 3 3 0 3 9.68 0.00 9.68 33.33 0.00 100.00 0.00 9 22 31 29.03 70.97 Tests Source DF −LogLike RSquare (U) Model 3 10.885300 0.5829 Error 27 7.790348 C. Total 30 18.675648 N 31 Test ChiSquare Prob > ChiSq Likelihood Ratio 21.771 <.0001 Pearson 19.560 0.0002

APPENDIX 20 Mortality Risk Tests Between Groups Test ChiSquare DF Prob > ChiSq Log-Rank 25.8169 3 <.0001 Wilcoxon 23.3328 3 <.0001

REFERENCES

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1-51. (canceled)
 52. A method for identifying patients who are likely to develop sepsis in response to an infection, the method comprising determining the level of mRNA for a biological marker in a sample from a patient.
 53. The method as claimed in claim 52 wherein the biological markers is a cytokine.
 54. The method as claimed in claim 53 wherein the cytokine is selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.
 55. The method as claimed in claim 53 wherein the cytokine is selected from one or more of TNFα, IL-10, IFNγ, IL-23, and IL-27.
 56. The method as claimed in claim 52 comprising the steps of:— obtaining a sample; extracting messenger RNA (mRNA) from the sample; synthesising complementary DNA (cDNA); and amplifying and quantifying cDNA for a biological marker(s) wherein the cDNA is amplified and quantified as a surrogate for mRNA and the level of cDNA provides specific data for mRNA levels in the sample.
 57. The method as claimed in claim 56 wherein the sample is a blood sample.
 58. The method as claimed in claim 57 wherein the sample is mononuclear cells from a peripheral blood sample, or white cells isolated in the Buffy Coat layer of a peripheral blood sample.
 59. The method as claimed in claim 57 wherein the blood sample is lysed prior to extracting mRNA.
 60. The method as claimed in claim 56 wherein the biological marker(s) are amplified and quantified using real time polymerase chain reaction.
 61. The method as claimed in claim 56 wherein the mRNA is measured in absolute terms by reference to a calibration curve constructed from a standard sample of DNA and normalised to a house keeping gene.
 62. The method as claimed in claim 52 wherein the biological marker is IL-10 and an mRNA copy number of about 426 copies or more per 10 million copies of a house keeping gene in a sample identifies patients who are likely to develop sepsis in response to an infection.
 63. The method as claimed in claim 52 wherein the biological marker is IFNγ and an mRNA copy number of about 240 copies or less per 10 million copies of a house keeping gene in a sample identifies patients who are likely to develop sepsis in response to an infection.
 64. The method as claimed in claim 52 wherein the biological marker is IL-23 and an mRNA copy number of about 1824 copies or more per 10 million copies of a house keeping gene in a sample identifies patients who are likely to develop sepsis in response to an infection.
 65. The method as claimed in claim 52 wherein the biological marker is IL-27 and an mRNA copy number of about 200 copies or less per 10 million copies of a house keeping gene in a sample identifies patients who are likely to develop sepsis in response to an infection.
 66. A binary scoring system for identifying patients who are likely to develop sepsis in response to an infection, the scoring system comprising determining the level of mRNA for a plurality of biological markers in a sample from a patient and assigning a score to the biological marker based in the mRNA level.
 67. The scoring system as claimed in claim 66 wherein the biological markers are cytokines.
 68. The method as claimed in claim 67 wherein the cytokines are selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.
 69. The method as claimed in claim 66 wherein the biological markers are IL-10 and IFNγ.
 70. The method as claimed in claim 69 wherein IL-10 with a copy number of 252 copies or more per 10 million copies of a house keeping gene is assigned a score of 1 and IFNγ with a copy number of 230 copies or less per 10 million copies of house keeping gene is assigned a score of
 1. 71. The method as claimed in claim 70 wherein a cumulative score of IL-10 and IFNγ of 1 or more identifies patients who are likely to develop sepsis in response to an infection.
 72. The method as claimed in claim 66 wherein the biological markers are IL-10 and IFNγ and TNFα.
 73. The method as claimed in claim 72 wherein IL-10 with a copy number of 660 copies or more per 10 million copies of a house keeping gene is assigned a score of 1, IFNγ with a copy number of 188 copies or less per 10 million copies of house keeping gene is assigned a score of 1, and TNFα with a copy number of 21380 copies or less per 10 million copies of a house keeping gene is assigned a score of
 1. 74. The method as claimed in claim 73 wherein a cumulative score of IL-10, IFNγ and TNFα of 1 or more identifies patients who are likely to develop sepsis in response to an infection.
 75. A method for monitoring the progress of sepsis in a patient, the method comprising determining the level of mRNA for a biological marker in a sample from a patient.
 76. The method as claimed in claim 75 wherein the biological marker is a cytokine.
 77. The method as claimed in claim 76 wherein the cytokine is selected from one or more of TNFα, IL-10, IFNγ, IL-12, IL-23, IL-27, IKBL, IL-4, TGFβ-1, IL-17 and IL-6.
 78. The method as claimed in claim 75 comprising the steps of:— obtaining a sample; extracting messenger RNA (mRNA) from the sample; synthesising complementary DNA (cDNA); and amplifying and quantifying cDNA for a biological marker(s) wherein the cDNA is amplified and quantified as a surrogate for mRNA and the level of cDNA provides specific data for mRNA levels in the sample.
 79. The method as claimed in claim 78 wherein the test sample is a blood sample.
 80. The method as claimed in claim 79 wherein the test sample is mononuclear cells from a peripheral blood sample, or white cells isolated in the Buffy Coat layer of a peripheral blood sample.
 81. The method as claimed in claim 79 wherein the blood sample is lysed prior to extracting mRNA.
 82. The method as claimed in claim 78 wherein the biological marker is amplified and quantified using real time polymerase chain reaction.
 83. The method as claimed in claim 78 wherein the level of mRNA is measured in absolute terms by reference to a calibration curve constructed from a standard sample of DNA and normalised to a house keeping gene.
 84. The method for treating sepsis in a patient comprising monitoring the progress of sepsis by a method as claimed claim 78 and, dependent on the level of mRNA of the biological marker, administering a medicament.
 85. The method as claimed in claim 84 wherein the medicaments comprises IFNγ.
 86. The method as claimed in claim 84 wherein the medicament is a medicament which blocks or antagonises the effects of IL-6.
 87. The method as claimed in any of claims 84 wherein the medicament is a medicament which blocks or antagonises the effects of IL-6 and or IL-10.
 88. A method of predicting mortality in patients with sepsis based on a ratio of mRNA levels between biological markers in the sample from the patient.
 89. The method as claimed in claim 88 wherein the biological markers are cytokines.
 90. The method as claimed in claim 88 wherein the biological markers are IL-10 and interferon gamma.
 91. The method as claimed in claim 90 wherein a ratio between IL-10 and interferon gamma of from about 6 to about 1 predicts mortality.
 92. The method as claimed in claim 90 wherein a ratio between IL-10 and interferon gamma of from about 4.52 to about 1.8 predicts mortality.
 93. The method as claimed in claim 90 wherein a ratio between IL-10 and interferon gamma of about 2.85 predicts mortality.
 94. The method as claimed in claim 88 wherein the biological markers are IL-23 and IL-27.
 95. The method as claimed in claim 94 wherein a ratio between IL-23 and IL-27 of from about 4 to about 0.05 predicts mortality.
 96. The method as claimed in claim 94 wherein a ratio between IL-23 and IL-27 of from about 2.6 to about 0.13 predicts mortality.
 97. The method as claimed in claim 94 wherein a ratio between IL-23 and IL-27 of about 1.45 predicts mortality.
 98. The method as claimed in claim 88 wherein predicting mortality in patients with sepsis is based on a score attributed to the ratio of mRNA levels between the ratio of IL-10:IFNγ and the ratio of IL-27:IL-23. 